Computer-rendered images can be a powerful tool for the analysis of data representing real-world objects, structures and phenomena. For example, detailed images are often produced by medical scanning devices that clinicians can use to help diagnose patients. The devices producing these images include magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT), positron emission tomography (PET) and ultrasound scanners. The images produced by these medical scanning devices can be two-dimensional images or three-dimensional volumetric images. In addition, sequences of two- or three-dimensional images can be produced to give a further temporal dimension to the images.
However, the large quantity of the data contained within such images means that the user can spend a significant amount of time just searching for the relevant part of the image. For example, in the case of a medical scan a clinician can spend a significant amount of time just searching for the relevant part of the body (e.g. heart, kidney, liver, etc.) before looking for certain features (e.g. signs of cancer or anatomical anomalies) that can help a diagnosis.
Some techniques exist for the automatic detection and recognition of objects in images, which can reduce the time spent manually searching an image. For example, geometric methods include template matching and convolution techniques. For medical images, geometrically meaningful features can, for example, be used for the segmentation of the aorta and the airway tree. However, such geometric approaches have problems capturing invariance with respect to deformations (e.g. due to pathologies), changes in viewing geometry (e.g. cropping) and changes in intensity. In addition, they do not generalize to highly deformable structures such as some blood vessels.
Another example is an atlas-based technique. An atlas is a hand-classified image, which is mapped to a subject image by deforming the atlas until it closely resembles the subject. This technique is therefore dependent on the availability of good atlases. In addition, the conceptual simplicity of such algorithms is in contrast to the requirement for accurate, deformable algorithms for registering the atlas with the subject. In medical applications, an issue with n-dimensional registration is in selecting the appropriate number of degrees of freedom of the underlying geometric transformation; especially as it depends on the level of rigidity of each organ/tissue. In addition, the optimal choice of the reference atlas can be complex (e.g. selecting separate atlases for an adult male body, a child, or a woman, each of which can be contrast enhanced or not). Atlas-based techniques can also be computationally inefficient.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known image analysis techniques.